Senior AI Engineer - AI Tech Lead

• Location: Paris, Châtelet – Hybrid (2 days on-site / 3 days remote)

• Contract: Full-time (permanent)

• Team: Technology

• Compensation: €75,000–€85,000 gross annual package (base + 12% target variable) + BSPCE (equity), depending on step

We're living through the biggest technological shift since the internet. Generative AI is changing how people work - yet most teams barely scratch the surface, unsure how to turn the hype into real, everyday value. Closing that gap is exactly what Mendo does.

Mendo is the AI Transformation Platform for enterprise clients. Rather than adding yet another tool to the stack, we work right inside the AI tools teams already use - Microsoft 365 Copilot, ChatGPT, Gemini, Claude, and internal agents. From there, we guide people toward proven use cases, certify their skills, and give organizations a clear view of what's actually driving value.

And it's working. We support 100+ enterprise clients - including EY, PwC, Crédit Agricole, SNCF, La Poste, ENGIE, Groupe SEB, and Novo Nordisk - with 200+ use cases live, a user NPS of 64, and 3x GenAI ROI within months. We've raised €12M following our Series A and we're scaling fast.

Join us and you won’t just ship software. You’ll shape how leading organizations adopt the defining technology of our era, and help keep people at the center of the AI revolution rather than left behind by it. We believe humans have a vital role to play in this revolution, and we’re building the platform that keeps them there.

Make AI a core strength of Mendo. We already ship AI across the product: proprietary small language models running on people’s own devices, retrieval and classification pipelines, and the analytics that tell organizations what drives value. You’ll own where it goes next.

Reporting directly to Arjun, you’ll set the AI roadmap, make the build/buy, architecture and cost calls, and define the quality bar every squad ships against. You stay hands-on: you train, evaluate and operate models yourself. As the practice grows, there is a path to building and leading an AI team, though that’s an opportunity, not a day-one expectation.

• Set and prioritize Mendo’s AI roadmap with product and engineering leadership, based on user impact and evidence rather than hype.

• Make the build/buy, architecture, and model choices, and own the trade-offs between cost, latency, and quality.

• Turn new AI use cases from the squads into clear decisions: prototype fast, measure honestly, and say no when the numbers don’t hold.

• Explain AI trade-offs clearly to non-technical colleagues so decisions get made quickly.

• Define Mendo’s standards for evaluation (golden sets, LLM-as-judge, regression tests) and for MLOps, and make sure every squad ships against them.

• Review the AI work shipped across the product, not just within your own scope.

• Keep our models, prompts, and pipelines current as the underlying tooling and APIs evolve.

• Train, fine-tune, and evaluate our proprietary language models, including the small models that run on-device.

• Curate and maintain the datasets, golden sets, and evaluation harnesses those models are judged against.

• Own the health of AI features in production: catch silent regressions before users do, and debug incidents end to end, from retrieval quality to prompt drift to model behaviour on a client’s machine.

• Mentor the AI engineer already on the team from day one, and support the engineers who work with you on AI topics.

• Help hire the next AI profiles at Mendo.

• Document what you build and why, so the practice compounds instead of living in one person’s head.

• Languages: Python for AI/ML work, TypeScript across the product.

• On-device / SLMs: GGUF models running in-browser via WebLLM and wllama (llama.cpp compiled to WASM), packaged into our browser and desktop connectors.

• LLMs & GenAI: OpenAI APIs, retrieval-augmented generation, and embeddings, integrated into Microsoft 365 Copilot, ChatGPT, Gemini, Claude, and internal agents.

• ML tooling: sentence-transformers, HuggingFace Transformers, scikit-learn, and TensorFlow, served through a Python service.

• Data: MongoDB and Redis.

• Infra & deployment: Azure / AKS with Helm, Docker containers, GitHub Actions CI/CD, Datadog observability.

• AI-augmented engineering: Claude Code / Cursor, MCP tooling, and internal AI agents are part of the daily workflow — not a bonus.

This stack is always evolving, and we're counting on you to help us evolve and improve it.

• 6+ years in ML / AI engineering, with AI models or features you shipped and then operated at scale in production.

• Proven technical leadership: you’ve set engineering standards, made architecture calls others built on, and mentored engineers.

• Hands-on with LLM fine-tuning, RAG, and embeddings, and with evaluating them (golden sets, LLM-as-judge, regression tests).

• Experience monitoring and optimising AI systems in production for cost, latency, and quality.

• Solid software engineering fundamentals (tests, CI, observability): you operate what you ship.

• Comfortable in Python and able to work across a TypeScript product codebase.

• Experience with small language models (SLMs) and on-device / on-prem model deployment is a strong plus.

• Decision-maker: you make the call on architecture and cost, and you can say no with evidence.

• Pragmatic and product-minded: user impact over technical novelty.

• You lead through influence across squads you don’t manage.

• Excellent command of French and English, both technical and professional.

• Natural rigor and a feel for evaluation-driven development.

• Genuine curiosity for a fast-moving AI landscape, without chasing hype.

• Strong team spirit and a sense of priorities in a fast-moving scale-up.

• Alignment with our values: Learning, Innovation, Excellence, Kindness, Transparency.

• Competitive package: €75,000–€85,000 gross annual package (base + 12% target variable) depending on step, plus BSPCE (equity) under our Series A policy.

• Real ownership: you set the direction of AI at Mendo, for a product used by 100+ enterprise clients.

• Real AI, not slides: proprietary on-device models, production RAG, and a codebase where AI is already shipped. You’ll shape it, not argue for it.

• Room to grow: a path to building and leading an AI team as the practice grows.

• A learning environment: a team passionate about new technologies and generative AI.

• A stimulating workplace: a young, supportive culture where everyone helps each other.

• Flexibility: a hybrid model with 2 days on-site and 3 days remote.

• Meaningful mission: helping make AI accessible and impactful for everyone.